Energy Efficient RRAM Spiking Neural Network for Real Time Classification

Inspired by the human brain’s function and efficiency, neuro-morphic computing offers a promising solution for a wide set of tasks, ranging from brain machine interfaces to real-time classification. The spiking neural network (SNN), which en-codes and processes information with bionic spikes, is an emerging neuromorphic model with great potential to dras-tically promote the performance and efficiency of comput-ing systems. However, an energy efficient hardware imple-mentation and the difficulty of training the model signifi-cantly limit the application of the spiking neural network. In this work, we address these issues by building an SNN-based energy efficient system for real time classification with metal-oxide resistive switching random-access memory (R-RAM) devices. We implement different training algorithms of SNN, including Spiking Time Dependent Plasticity (STD-P) and Neural Sampling method. Our RRAM SNN systems for these two training algorithms show good power efficiency and recognition performance on realtime classification tasks, such as the MNIST digit recognition. Finally, we propose a possible direction to further improve the classification accu-racy by boosting multiple SNNs. 1